Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies on multivariable models, which integrate ...
Ensemble integrating three architectures achieved area under the curve of 0.9208, outperforming individual models.
Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.
A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
Abstract: The Modular Multilevel Converter (MMC) has garnered significant interest recently due to its superior harmonic performance and improved efficiency in high-voltage direct current electrical ...
Less instrumentation. More insight. Physics-informed virtual sensors are shifting condition monitoring from isolated pilots to scalable, physics-based intelligence across assets. Here’s how SciML can ...
Objective: To evaluate and to compare machine learning models for predicting hypertension in patients with diabetes using routine clinical variables. Methods: Using Behavioral Risk Factor Surveillance ...
ABSTRACT: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for millions of deaths each year according to the World Health Organization (WHO). Early detection of ...
Introduction: This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG).
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